DBLP bibliography

BACKGROUND: In children with spastic cerebral palsy (CP), the most common motor dysfunction is pathological gait. OBJECTIVE: To evaluate the effectiveness of measures for an individual CP case. METHODS: The case of one spastic diplegia child has been analyzed. Both lower extremities and spine were examined under three gait conditions: 1) barefoot, 2) with ankle-foot orthoses (AFOs) and thoracolumbosacral spinal orthosis (TLSO), and 3) with TLSO only. Spatiotemporal gait and kinematic parameters of the pelvic, hip, knee, ankle joints, and spine were obtained using Vicon Plug-in-Gait model. The difference (Δ) between the measured values and normative ranges was calculated to determine the efficiency of the orthoses. RESULTS: Significant differences were found in kinematic and spatiotemporal parameters comparing results between conditions and body sides. The effectiveness of the measures was confirmed by the smallest Δ values in the double and single support time with the AFOs/TLSO and in the stride and stance time with TLSO. CONCLUSIONS: Based on the study results, the best stability of the spine, ankle plantarflexion, and knee hyperextension is achieved with the AFOs/TLSO; therefore, this combination of measures was considered the most effective. However, not only quantitative parameters should be taken into account, but also the child’s willingness and comfort.

BACKGROUND: Gait can be affected by diseases such as Parkinson’s disease (PD), which lead to alterations like shuffle gait or loss of balance. PD diagnosis is based on subjective measures to generate a score using the Unified Parkinson’s Disease Rating Scale (UPDRS). To improve clinical assessment accuracy, gait analysis can utilise linear and nonlinear methods. A nonlinear method called the Lyapunov exponent (LE) is being used to identify chaos in dynamic systems. This article presents an application of LE for diagnosing PD. OBJECTIVE: The objectives were to use the largest Lyapunov exponents (LaLyEx), sample entropy (SampEn) and root mean square (RMS) to assess the gait of subjects diagnosed with PD; to verify the applicability of these parameters to distinguish between people with PD and healthy controls (CO); and to differentiate subjects within the PD group according to the UPDRS assessment. METHODS: The subjects were divided into the CO group (n= 12) and the PD group (n= 14). The PD group was also divided according to the UPDRS score: UPDRS 0 (n= 7) and UPDRS 1 (n= 7). Kinematic data of lower limbs were measured using inertial measurement units (IMU) and nonlinear parameters (LaLyEx, SampEn and RMS) were calculated. RESULTS: There were significant differences between the CO and PD groups for RMS, SampEn and the LaLyEx. After dividing the PD group according to the UPDRS score, there were significant differences in LaLyEx and RMS. CONCLUSIONS: The selected parameters can be used to distinguish people with PD from CO subjects, and separate people with PD according to the UPDRS score.

This special section in Technology and Health Care contains a collection of selected papers from 13th International Conference BIOMDLORE 2021, which was held on 21–23 October 2021 in Vilnius, Lithuania.